An Intelligent Optimal Secure Framework for Malicious Events Prevention in IOT Cloud Networks

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Published: Apr 4, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i3.6328
Keywords:
Attack prevention, Security framework, Cloud computing, Neural Network, Internet of Things, Cloud storage

Main Article Content

Majjaru Chandra Babu
Research Scholar, School of Information Technology and Engineering, VIT University, Vellore, Tamil Nadu, India
Senthilkumar K
Associate Professor, School of Computer Science Engineering, VIT University, Vellore, Tamil Nadu, India

Abstract

The intrusion is considered a significant problematic parameter in Cloud networks. Thus, an efficient mechanism is required to avoid intrusion and provide more security to the cloud system. Therefore, the novel Artificial Bee-based Elman Neural Security Framework (ABENSF) is developed in this article. The developed model rescales the raw dataset using the pre-processing function. Moreover, the artificial bee's optimal fitness function is integrated into the feature extraction phase to track and extract the attack features. In addition, the monitoring mechanism in the developed model provides high security to the network by preventing attacks. Thus, the tracking and monitoring functions avoid intrusion by eliminating known and unknown attacks. The presented work was designed and validated with an NSL-KDD dataset in python software. Finally, the performance parameters of the presented work are estimated and verified with the existing techniques in a comparative analysis. The comparative performance shows that the developed model has earned better outcomes than others.

Article Details

How to Cite
Babu, M. C. ., & K, S. . (2023). An Intelligent Optimal Secure Framework for Malicious Events Prevention in IOT Cloud Networks. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 117–127. https://doi.org/10.17762/ijritcc.v11i3.6328
Issue
Vol. 11 No. 3 (2023): March (2023) Issue
Section
Articles

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